001015186 001__ 1015186
001015186 005__ 20231023093628.0
001015186 0247_ $$2doi$$a10.1109/NANO58406.2023.10231169
001015186 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-03582
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001015186 037__ $$aFZJ-2023-03582
001015186 1001_ $$0P:(DE-Juel1)180323$$aRuzaeva, Karina$$b0$$ufzj
001015186 1112_ $$a2023 IEEE 23rd International Conference on Nanotechnology (NANO)$$cJeju City$$d2023-07-02 - 2023-07-05$$wKorea
001015186 245__ $$aInstance Segmentation of Dislocations in TEM Images
001015186 260__ $$bIEEE$$c2023
001015186 300__ $$a1-6
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001015186 520__ $$aQuantitative Transmission Electron Microscopy (TEM) during in-situ straining experiment is able to reveal the motion of dislocations - linear defects in the crystal lattice of metals. In the domain of materials science, the knowledge about the location and movement of dislocations is important for creating novel materials with superior properties. A longstanding problem, however, is to identify the position and extract the shape of dislocations, which would ultimately help to create a digital twin of such materials. In this work, we quantitatively compare state-of-the-art instance segmentation methods, including Mask R-CNN and YOLOv8. The dislocation masks as the results of the instance segmentation are converted to mathematical lines, enabling quantitative analysis of dislocation length and geometry - important information for the domain scientist, which we then propose to include as a novel length-aware quality metric for estimating the network performance. Our segmentation pipeline shows a high accuracy suitable for all domain-specific, further post-processing. Additionally, our physics-based metric turns out to perform much more consistently than typically used pixel-wise metrics.
001015186 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001015186 588__ $$aDataset connected to CrossRef Conference
001015186 7001_ $$0P:(DE-Juel1)186834$$aGovind, Kishan$$b1$$ufzj
001015186 7001_ $$0P:(DE-HGF)0$$aLegros, Marc$$b2
001015186 7001_ $$0P:(DE-Juel1)186075$$aSandfeld, Stefan$$b3$$eCorresponding author$$ufzj
001015186 773__ $$a10.1109/NANO58406.2023.10231169
001015186 8564_ $$uhttps://juser.fz-juelich.de/record/1015186/files/2309.03499.pdf$$yOpenAccess
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001015186 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186834$$aForschungszentrum Jülich$$b1$$kFZJ
001015186 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186075$$aForschungszentrum Jülich$$b3$$kFZJ
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001015186 9141_ $$y2023
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001015186 9201_ $$0I:(DE-Juel1)IAS-9-20201008$$kIAS-9$$lMaterials Data Science and Informatics$$x0
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